Rotating machines have been widely used in industrial engineering. The fault diagnosis of
rotating machines plays a vital important role to reduce the catastrophic failures and heavy …

Blind deconvolution has been proved to be an effective method for fault detection since it
can recover periodic impulses from mixed fault signals convoluted by noise and periodic …

Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of
practical importance. For this purpose, ensemble transfer convolutional neural networks …

Early fault prognosis of bearing is a very meaningful yet challenging task to improve the
security of rotating machinery. For this purpose, a novel method based on enhanced deep …

Fault diagnosis of rotating machinery (FDRM) has attracted continuous attention because of
its great importance to industrial engineering, promoting the healthy and prosperous …

Fault diagnosis of rotating machinery serves an important role in informing system operation
and predictive maintenance decisions. To quantify the fault information from vibrational …

Entropy, as a complexity measure, has been widely applied for time series analysis. One
preeminent example is the design of machine condition monitoring and industrial fault …

Aiming at the difficulty of mechanical fault diagnosis with small samples, an intelligent fault
diagnosis method for rotating machinery is proposed based on semi-supervised graph …

When wind turbine driving system (WTDS) undergoes abnormal conditions, the fault
information hidden in WTDS scatters over multiple signal channels and hence inadequate …

In this article, a fault diagnosis scheme based on multiscale diversity entropy (MDE) and
extreme learning machine (ELM) is presented. First, a novel entropy method called diversity …